Commit
·
dcacd5e
1
Parent(s):
8b30921
Upload model
Browse files- base_model.py +16 -0
- blocks.py +383 -0
- config.json +12 -0
- configuration_dptdepth.py +24 -0
- modeling_dptdepth.py +36 -0
- models.py +126 -0
- pytorch_model.bin +3 -0
- vit.py +576 -0
base_model.py
ADDED
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import torch
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class BaseModel(torch.nn.Module):
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def load(self, path):
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"""Load model from file.
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Args:
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path (str): file path
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"""
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parameters = torch.load(path, map_location=torch.device("cpu"))
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if "optimizer" in parameters:
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parameters = parameters["model"]
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self.load_state_dict(parameters)
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blocks.py
ADDED
@@ -0,0 +1,383 @@
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import torch
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import torch.nn as nn
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from .vit import (
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_make_pretrained_vitb_rn50_384,
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_make_pretrained_vitl16_384,
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_make_pretrained_vitb16_384,
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forward_vit,
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)
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def _make_encoder(
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backbone,
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features,
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use_pretrained,
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groups=1,
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expand=False,
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exportable=True,
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hooks=None,
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use_vit_only=False,
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use_readout="ignore",
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enable_attention_hooks=False,
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):
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if backbone == "vitl16_384":
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pretrained = _make_pretrained_vitl16_384(
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use_pretrained,
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hooks=hooks,
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use_readout=use_readout,
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enable_attention_hooks=enable_attention_hooks,
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)
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scratch = _make_scratch(
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[256, 512, 1024, 1024], features, groups=groups, expand=expand
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) # ViT-L/16 - 85.0% Top1 (backbone)
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elif backbone == "vitb_rn50_384":
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pretrained = _make_pretrained_vitb_rn50_384(
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use_pretrained,
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hooks=hooks,
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use_vit_only=use_vit_only,
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use_readout=use_readout,
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enable_attention_hooks=enable_attention_hooks,
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)
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scratch = _make_scratch(
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[256, 512, 768, 768], features, groups=groups, expand=expand
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) # ViT-H/16 - 85.0% Top1 (backbone)
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elif backbone == "vitb16_384":
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pretrained = _make_pretrained_vitb16_384(
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use_pretrained,
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hooks=hooks,
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use_readout=use_readout,
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enable_attention_hooks=enable_attention_hooks,
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)
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scratch = _make_scratch(
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[96, 192, 384, 768], features, groups=groups, expand=expand
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) # ViT-B/16 - 84.6% Top1 (backbone)
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elif backbone == "resnext101_wsl":
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pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
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scratch = _make_scratch(
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[256, 512, 1024, 2048], features, groups=groups, expand=expand
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) # efficientnet_lite3
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else:
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print(f"Backbone '{backbone}' not implemented")
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assert False
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return pretrained, scratch
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def _make_scratch(in_shape, out_shape, groups=1, expand=False):
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scratch = nn.Module()
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out_shape1 = out_shape
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out_shape2 = out_shape
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out_shape3 = out_shape
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out_shape4 = out_shape
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if expand == True:
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out_shape1 = out_shape
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out_shape2 = out_shape * 2
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out_shape3 = out_shape * 4
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out_shape4 = out_shape * 8
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scratch.layer1_rn = nn.Conv2d(
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in_shape[0],
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out_shape1,
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kernel_size=3,
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stride=1,
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padding=1,
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bias=False,
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groups=groups,
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)
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scratch.layer2_rn = nn.Conv2d(
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in_shape[1],
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out_shape2,
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kernel_size=3,
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stride=1,
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padding=1,
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bias=False,
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groups=groups,
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)
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scratch.layer3_rn = nn.Conv2d(
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in_shape[2],
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out_shape3,
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101 |
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kernel_size=3,
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102 |
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stride=1,
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103 |
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padding=1,
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104 |
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bias=False,
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105 |
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groups=groups,
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)
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107 |
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scratch.layer4_rn = nn.Conv2d(
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108 |
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in_shape[3],
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109 |
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out_shape4,
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110 |
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kernel_size=3,
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111 |
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stride=1,
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112 |
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padding=1,
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113 |
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bias=False,
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114 |
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groups=groups,
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)
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+
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return scratch
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+
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119 |
+
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120 |
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def _make_resnet_backbone(resnet):
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pretrained = nn.Module()
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122 |
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pretrained.layer1 = nn.Sequential(
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resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
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)
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125 |
+
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pretrained.layer2 = resnet.layer2
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127 |
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pretrained.layer3 = resnet.layer3
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128 |
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pretrained.layer4 = resnet.layer4
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129 |
+
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130 |
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return pretrained
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131 |
+
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132 |
+
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133 |
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def _make_pretrained_resnext101_wsl(use_pretrained):
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134 |
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resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
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135 |
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return _make_resnet_backbone(resnet)
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136 |
+
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137 |
+
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class Interpolate(nn.Module):
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"""Interpolation module."""
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140 |
+
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def __init__(self, scale_factor, mode, align_corners=False):
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"""Init.
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143 |
+
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144 |
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Args:
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145 |
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scale_factor (float): scaling
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146 |
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mode (str): interpolation mode
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147 |
+
"""
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148 |
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super(Interpolate, self).__init__()
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149 |
+
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150 |
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self.interp = nn.functional.interpolate
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self.scale_factor = scale_factor
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152 |
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self.mode = mode
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153 |
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self.align_corners = align_corners
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154 |
+
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155 |
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def forward(self, x):
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156 |
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"""Forward pass.
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157 |
+
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158 |
+
Args:
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159 |
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x (tensor): input
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160 |
+
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161 |
+
Returns:
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162 |
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tensor: interpolated data
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163 |
+
"""
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164 |
+
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165 |
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x = self.interp(
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x,
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167 |
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scale_factor=self.scale_factor,
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168 |
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mode=self.mode,
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169 |
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align_corners=self.align_corners,
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170 |
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)
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171 |
+
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172 |
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return x
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173 |
+
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174 |
+
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175 |
+
class ResidualConvUnit(nn.Module):
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176 |
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"""Residual convolution module."""
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177 |
+
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178 |
+
def __init__(self, features):
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179 |
+
"""Init.
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180 |
+
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181 |
+
Args:
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182 |
+
features (int): number of features
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183 |
+
"""
|
184 |
+
super().__init__()
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185 |
+
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186 |
+
self.conv1 = nn.Conv2d(
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features, features, kernel_size=3, stride=1, padding=1, bias=True
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188 |
+
)
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189 |
+
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190 |
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self.conv2 = nn.Conv2d(
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191 |
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features, features, kernel_size=3, stride=1, padding=1, bias=True
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192 |
+
)
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193 |
+
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194 |
+
self.relu = nn.ReLU(inplace=True)
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195 |
+
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196 |
+
def forward(self, x):
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197 |
+
"""Forward pass.
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198 |
+
|
199 |
+
Args:
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200 |
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x (tensor): input
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201 |
+
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202 |
+
Returns:
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203 |
+
tensor: output
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204 |
+
"""
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205 |
+
out = self.relu(x)
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206 |
+
out = self.conv1(out)
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207 |
+
out = self.relu(out)
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208 |
+
out = self.conv2(out)
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209 |
+
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210 |
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return out + x
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211 |
+
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212 |
+
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213 |
+
class FeatureFusionBlock(nn.Module):
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214 |
+
"""Feature fusion block."""
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215 |
+
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216 |
+
def __init__(self, features):
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217 |
+
"""Init.
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218 |
+
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219 |
+
Args:
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220 |
+
features (int): number of features
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221 |
+
"""
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222 |
+
super(FeatureFusionBlock, self).__init__()
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223 |
+
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224 |
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self.resConfUnit1 = ResidualConvUnit(features)
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225 |
+
self.resConfUnit2 = ResidualConvUnit(features)
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226 |
+
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227 |
+
def forward(self, *xs):
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228 |
+
"""Forward pass.
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229 |
+
|
230 |
+
Returns:
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231 |
+
tensor: output
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232 |
+
"""
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233 |
+
output = xs[0]
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234 |
+
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235 |
+
if len(xs) == 2:
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236 |
+
output += self.resConfUnit1(xs[1])
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237 |
+
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238 |
+
output = self.resConfUnit2(output)
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239 |
+
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240 |
+
output = nn.functional.interpolate(
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241 |
+
output, scale_factor=2, mode="bilinear", align_corners=True
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242 |
+
)
|
243 |
+
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244 |
+
return output
|
245 |
+
|
246 |
+
|
247 |
+
class ResidualConvUnit_custom(nn.Module):
|
248 |
+
"""Residual convolution module."""
|
249 |
+
|
250 |
+
def __init__(self, features, activation, bn):
|
251 |
+
"""Init.
|
252 |
+
|
253 |
+
Args:
|
254 |
+
features (int): number of features
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255 |
+
"""
|
256 |
+
super().__init__()
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257 |
+
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258 |
+
self.bn = bn
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259 |
+
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260 |
+
self.groups = 1
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261 |
+
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262 |
+
self.conv1 = nn.Conv2d(
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263 |
+
features,
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264 |
+
features,
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265 |
+
kernel_size=3,
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266 |
+
stride=1,
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267 |
+
padding=1,
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268 |
+
bias=not self.bn,
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269 |
+
groups=self.groups,
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270 |
+
)
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271 |
+
|
272 |
+
self.conv2 = nn.Conv2d(
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273 |
+
features,
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274 |
+
features,
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275 |
+
kernel_size=3,
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276 |
+
stride=1,
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277 |
+
padding=1,
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278 |
+
bias=not self.bn,
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279 |
+
groups=self.groups,
|
280 |
+
)
|
281 |
+
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282 |
+
if self.bn == True:
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283 |
+
self.bn1 = nn.BatchNorm2d(features)
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284 |
+
self.bn2 = nn.BatchNorm2d(features)
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285 |
+
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286 |
+
self.activation = activation
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287 |
+
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288 |
+
self.skip_add = nn.quantized.FloatFunctional()
|
289 |
+
|
290 |
+
def forward(self, x):
|
291 |
+
"""Forward pass.
|
292 |
+
|
293 |
+
Args:
|
294 |
+
x (tensor): input
|
295 |
+
|
296 |
+
Returns:
|
297 |
+
tensor: output
|
298 |
+
"""
|
299 |
+
|
300 |
+
out = self.activation(x)
|
301 |
+
out = self.conv1(out)
|
302 |
+
if self.bn == True:
|
303 |
+
out = self.bn1(out)
|
304 |
+
|
305 |
+
out = self.activation(out)
|
306 |
+
out = self.conv2(out)
|
307 |
+
if self.bn == True:
|
308 |
+
out = self.bn2(out)
|
309 |
+
|
310 |
+
if self.groups > 1:
|
311 |
+
out = self.conv_merge(out)
|
312 |
+
|
313 |
+
return self.skip_add.add(out, x)
|
314 |
+
|
315 |
+
# return out + x
|
316 |
+
|
317 |
+
|
318 |
+
class FeatureFusionBlock_custom(nn.Module):
|
319 |
+
"""Feature fusion block."""
|
320 |
+
|
321 |
+
def __init__(
|
322 |
+
self,
|
323 |
+
features,
|
324 |
+
activation,
|
325 |
+
deconv=False,
|
326 |
+
bn=False,
|
327 |
+
expand=False,
|
328 |
+
align_corners=True,
|
329 |
+
):
|
330 |
+
"""Init.
|
331 |
+
|
332 |
+
Args:
|
333 |
+
features (int): number of features
|
334 |
+
"""
|
335 |
+
super(FeatureFusionBlock_custom, self).__init__()
|
336 |
+
|
337 |
+
self.deconv = deconv
|
338 |
+
self.align_corners = align_corners
|
339 |
+
|
340 |
+
self.groups = 1
|
341 |
+
|
342 |
+
self.expand = expand
|
343 |
+
out_features = features
|
344 |
+
if self.expand == True:
|
345 |
+
out_features = features // 2
|
346 |
+
|
347 |
+
self.out_conv = nn.Conv2d(
|
348 |
+
features,
|
349 |
+
out_features,
|
350 |
+
kernel_size=1,
|
351 |
+
stride=1,
|
352 |
+
padding=0,
|
353 |
+
bias=True,
|
354 |
+
groups=1,
|
355 |
+
)
|
356 |
+
|
357 |
+
self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
|
358 |
+
self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
|
359 |
+
|
360 |
+
self.skip_add = nn.quantized.FloatFunctional()
|
361 |
+
|
362 |
+
def forward(self, *xs):
|
363 |
+
"""Forward pass.
|
364 |
+
|
365 |
+
Returns:
|
366 |
+
tensor: output
|
367 |
+
"""
|
368 |
+
output = xs[0]
|
369 |
+
|
370 |
+
if len(xs) == 2:
|
371 |
+
res = self.resConfUnit1(xs[1])
|
372 |
+
output = self.skip_add.add(output, res)
|
373 |
+
# output += res
|
374 |
+
|
375 |
+
output = self.resConfUnit2(output)
|
376 |
+
|
377 |
+
output = nn.functional.interpolate(
|
378 |
+
output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
|
379 |
+
)
|
380 |
+
|
381 |
+
output = self.out_conv(output)
|
382 |
+
|
383 |
+
return output
|
config.json
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"DPTDepthModel"
|
4 |
+
],
|
5 |
+
"auto_map": {
|
6 |
+
"AutoConfig": "configuration_dptdepth.DPTDepthConfig",
|
7 |
+
"AutoModel": "modeling_dptdepth.DPTDepthModel"
|
8 |
+
},
|
9 |
+
"model_type": "dptdepth",
|
10 |
+
"torch_dtype": "float32",
|
11 |
+
"transformers_version": "4.27.3"
|
12 |
+
}
|
configuration_dptdepth.py
ADDED
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import List
|
2 |
+
|
3 |
+
from transformers import PretrainedConfig
|
4 |
+
|
5 |
+
"""
|
6 |
+
The configuration of a model is an object that
|
7 |
+
will contain all the necessary information to build the model.
|
8 |
+
The three important things to remember when writing you own configuration are the following:
|
9 |
+
- you have to inherit from PretrainedConfig,
|
10 |
+
- the __init__ of your PretrainedConfig must accept any kwargs,
|
11 |
+
- those kwargs need to be passed to the superclass __init__.
|
12 |
+
"""
|
13 |
+
|
14 |
+
|
15 |
+
class DPTDepthConfig(PretrainedConfig):
|
16 |
+
|
17 |
+
"""
|
18 |
+
Defining a model_type for your configuration is not mandatory,
|
19 |
+
unless you want to register your model with the auto classes."""
|
20 |
+
|
21 |
+
model_type = "dptdepth"
|
22 |
+
|
23 |
+
def __init__(self, **kwargs):
|
24 |
+
super().__init__(**kwargs)
|
modeling_dptdepth.py
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import Dict, Optional
|
2 |
+
|
3 |
+
from torch import Tensor, nn
|
4 |
+
from transformers import PreTrainedModel
|
5 |
+
|
6 |
+
from .configuration_dptdepth import DPTDepthConfig
|
7 |
+
from .models import DPTDepthModel as DPTDepth
|
8 |
+
|
9 |
+
|
10 |
+
class DPTDepthModel(PreTrainedModel):
|
11 |
+
"""
|
12 |
+
The line that sets the config_class is not mandatory,
|
13 |
+
unless you want to register your model with the auto classes
|
14 |
+
"""
|
15 |
+
|
16 |
+
config_class = DPTDepthConfig
|
17 |
+
|
18 |
+
def __init__(self, config: DPTDepthConfig):
|
19 |
+
super().__init__(config)
|
20 |
+
self.model = DPTDepth()
|
21 |
+
self.loss = nn.L1Loss()
|
22 |
+
|
23 |
+
"""
|
24 |
+
You can have your model return anything you want,
|
25 |
+
but returning a dictionary with the loss included when labels are passed,
|
26 |
+
will make your model directly usable inside the Trainer class.
|
27 |
+
Using another output format is fine as long as you are planning on
|
28 |
+
using your own training loop or another library for training.
|
29 |
+
"""
|
30 |
+
|
31 |
+
def forward(self, rgbs: Tensor, gts: Optional[Tensor] = None) -> Dict[str, Tensor]:
|
32 |
+
logits = self.model(rgbs)
|
33 |
+
if gts is not None:
|
34 |
+
loss = self.loss(logits, gts)
|
35 |
+
return {"loss": loss, "logits": logits}
|
36 |
+
return {"logits": logits}
|
models.py
ADDED
@@ -0,0 +1,126 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
from torch import Tensor
|
4 |
+
|
5 |
+
from .base_model import BaseModel
|
6 |
+
from .blocks import (
|
7 |
+
FeatureFusionBlock_custom,
|
8 |
+
Interpolate,
|
9 |
+
_make_encoder,
|
10 |
+
forward_vit,
|
11 |
+
)
|
12 |
+
|
13 |
+
|
14 |
+
def _make_fusion_block(features, use_bn):
|
15 |
+
return FeatureFusionBlock_custom(
|
16 |
+
features,
|
17 |
+
nn.ReLU(False),
|
18 |
+
deconv=False,
|
19 |
+
bn=use_bn,
|
20 |
+
expand=False,
|
21 |
+
align_corners=True,
|
22 |
+
)
|
23 |
+
|
24 |
+
|
25 |
+
class DPT(BaseModel):
|
26 |
+
def __init__(
|
27 |
+
self,
|
28 |
+
head,
|
29 |
+
features=256,
|
30 |
+
backbone="vitb_rn50_384",
|
31 |
+
readout="project",
|
32 |
+
channels_last=False,
|
33 |
+
use_bn=False,
|
34 |
+
enable_attention_hooks=False,
|
35 |
+
):
|
36 |
+
|
37 |
+
super(DPT, self).__init__()
|
38 |
+
|
39 |
+
self.channels_last = channels_last
|
40 |
+
|
41 |
+
hooks = {
|
42 |
+
"vitb_rn50_384": [0, 1, 8, 11],
|
43 |
+
"vitb16_384": [2, 5, 8, 11],
|
44 |
+
"vitl16_384": [5, 11, 17, 23],
|
45 |
+
}
|
46 |
+
|
47 |
+
# Instantiate backbone and reassemble blocks
|
48 |
+
self.pretrained, self.scratch = _make_encoder(
|
49 |
+
backbone,
|
50 |
+
features,
|
51 |
+
False, # Set to true of you want to train from scratch, uses ImageNet weights
|
52 |
+
groups=1,
|
53 |
+
expand=False,
|
54 |
+
exportable=False,
|
55 |
+
hooks=hooks[backbone],
|
56 |
+
use_readout=readout,
|
57 |
+
enable_attention_hooks=enable_attention_hooks,
|
58 |
+
)
|
59 |
+
|
60 |
+
self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
|
61 |
+
self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
|
62 |
+
self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
|
63 |
+
self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
|
64 |
+
|
65 |
+
self.scratch.output_conv = head
|
66 |
+
|
67 |
+
def forward(self, x: Tensor) -> Tensor:
|
68 |
+
if self.channels_last == True:
|
69 |
+
x.contiguous(memory_format=torch.channels_last)
|
70 |
+
|
71 |
+
layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
|
72 |
+
|
73 |
+
layer_1_rn = self.scratch.layer1_rn(layer_1)
|
74 |
+
layer_2_rn = self.scratch.layer2_rn(layer_2)
|
75 |
+
layer_3_rn = self.scratch.layer3_rn(layer_3)
|
76 |
+
layer_4_rn = self.scratch.layer4_rn(layer_4)
|
77 |
+
|
78 |
+
path_4 = self.scratch.refinenet4(layer_4_rn)
|
79 |
+
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
|
80 |
+
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
|
81 |
+
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
|
82 |
+
|
83 |
+
out = self.scratch.output_conv(path_1)
|
84 |
+
|
85 |
+
return out
|
86 |
+
|
87 |
+
|
88 |
+
class DPTDepthModel(DPT):
|
89 |
+
def __init__(
|
90 |
+
self, path=None, non_negative=True, scale=1.0, shift=0.0, invert=False, **kwargs
|
91 |
+
):
|
92 |
+
features = kwargs["features"] if "features" in kwargs else 256
|
93 |
+
|
94 |
+
self.scale = scale
|
95 |
+
self.shift = shift
|
96 |
+
self.invert = invert
|
97 |
+
|
98 |
+
head = nn.Sequential(
|
99 |
+
nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
|
100 |
+
Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
|
101 |
+
nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
|
102 |
+
nn.ReLU(True),
|
103 |
+
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
|
104 |
+
nn.ReLU(True) if non_negative else nn.Identity(),
|
105 |
+
nn.Identity(),
|
106 |
+
)
|
107 |
+
|
108 |
+
super().__init__(head, **kwargs)
|
109 |
+
|
110 |
+
if path is not None:
|
111 |
+
self.load(path)
|
112 |
+
|
113 |
+
def forward(self, x: Tensor) -> Tensor:
|
114 |
+
"""Input x of shape [b, c, h, w]
|
115 |
+
Return tensor of shape [b, c, h, w]
|
116 |
+
"""
|
117 |
+
inv_depth = super().forward(x)
|
118 |
+
|
119 |
+
if self.invert:
|
120 |
+
depth = self.scale * inv_depth + self.shift
|
121 |
+
depth[depth < 1e-8] = 1e-8
|
122 |
+
depth = 1.0 / depth
|
123 |
+
return depth
|
124 |
+
else:
|
125 |
+
return inv_depth
|
126 |
+
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:545a6e1dd3258ae359ccc400e2bf0e39f7411e9a6c8f55b1e60017b47125b1ab
|
3 |
+
size 492713165
|
vit.py
ADDED
@@ -0,0 +1,576 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
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|
|
|
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|
|
|
|
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|
|
|
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|
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|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
import timm
|
4 |
+
import types
|
5 |
+
import math
|
6 |
+
import torch.nn.functional as F
|
7 |
+
|
8 |
+
|
9 |
+
activations = {}
|
10 |
+
|
11 |
+
|
12 |
+
def get_activation(name):
|
13 |
+
def hook(model, input, output):
|
14 |
+
activations[name] = output
|
15 |
+
|
16 |
+
return hook
|
17 |
+
|
18 |
+
|
19 |
+
attention = {}
|
20 |
+
|
21 |
+
|
22 |
+
def get_attention(name):
|
23 |
+
def hook(module, input, output):
|
24 |
+
x = input[0]
|
25 |
+
B, N, C = x.shape
|
26 |
+
qkv = (
|
27 |
+
module.qkv(x)
|
28 |
+
.reshape(B, N, 3, module.num_heads, C // module.num_heads)
|
29 |
+
.permute(2, 0, 3, 1, 4)
|
30 |
+
)
|
31 |
+
q, k, v = (
|
32 |
+
qkv[0],
|
33 |
+
qkv[1],
|
34 |
+
qkv[2],
|
35 |
+
) # make torchscript happy (cannot use tensor as tuple)
|
36 |
+
|
37 |
+
attn = (q @ k.transpose(-2, -1)) * module.scale
|
38 |
+
|
39 |
+
attn = attn.softmax(dim=-1) # [:,:,1,1:]
|
40 |
+
attention[name] = attn
|
41 |
+
|
42 |
+
return hook
|
43 |
+
|
44 |
+
|
45 |
+
def get_mean_attention_map(attn, token, shape):
|
46 |
+
attn = attn[:, :, token, 1:]
|
47 |
+
attn = attn.unflatten(2, torch.Size([shape[2] // 16, shape[3] // 16])).float()
|
48 |
+
attn = torch.nn.functional.interpolate(
|
49 |
+
attn, size=shape[2:], mode="bicubic", align_corners=False
|
50 |
+
).squeeze(0)
|
51 |
+
|
52 |
+
all_attn = torch.mean(attn, 0)
|
53 |
+
|
54 |
+
return all_attn
|
55 |
+
|
56 |
+
|
57 |
+
class Slice(nn.Module):
|
58 |
+
def __init__(self, start_index=1):
|
59 |
+
super(Slice, self).__init__()
|
60 |
+
self.start_index = start_index
|
61 |
+
|
62 |
+
def forward(self, x):
|
63 |
+
return x[:, self.start_index :]
|
64 |
+
|
65 |
+
|
66 |
+
class AddReadout(nn.Module):
|
67 |
+
def __init__(self, start_index=1):
|
68 |
+
super(AddReadout, self).__init__()
|
69 |
+
self.start_index = start_index
|
70 |
+
|
71 |
+
def forward(self, x):
|
72 |
+
if self.start_index == 2:
|
73 |
+
readout = (x[:, 0] + x[:, 1]) / 2
|
74 |
+
else:
|
75 |
+
readout = x[:, 0]
|
76 |
+
return x[:, self.start_index :] + readout.unsqueeze(1)
|
77 |
+
|
78 |
+
|
79 |
+
class ProjectReadout(nn.Module):
|
80 |
+
def __init__(self, in_features, start_index=1):
|
81 |
+
super(ProjectReadout, self).__init__()
|
82 |
+
self.start_index = start_index
|
83 |
+
|
84 |
+
self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
|
85 |
+
|
86 |
+
def forward(self, x):
|
87 |
+
readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
|
88 |
+
features = torch.cat((x[:, self.start_index :], readout), -1)
|
89 |
+
|
90 |
+
return self.project(features)
|
91 |
+
|
92 |
+
|
93 |
+
class Transpose(nn.Module):
|
94 |
+
def __init__(self, dim0, dim1):
|
95 |
+
super(Transpose, self).__init__()
|
96 |
+
self.dim0 = dim0
|
97 |
+
self.dim1 = dim1
|
98 |
+
|
99 |
+
def forward(self, x):
|
100 |
+
x = x.transpose(self.dim0, self.dim1)
|
101 |
+
return x
|
102 |
+
|
103 |
+
|
104 |
+
def forward_vit(pretrained, x):
|
105 |
+
b, c, h, w = x.shape
|
106 |
+
|
107 |
+
glob = pretrained.model.forward_flex(x)
|
108 |
+
|
109 |
+
layer_1 = pretrained.activations["1"]
|
110 |
+
layer_2 = pretrained.activations["2"]
|
111 |
+
layer_3 = pretrained.activations["3"]
|
112 |
+
layer_4 = pretrained.activations["4"]
|
113 |
+
|
114 |
+
layer_1 = pretrained.act_postprocess1[0:2](layer_1)
|
115 |
+
layer_2 = pretrained.act_postprocess2[0:2](layer_2)
|
116 |
+
layer_3 = pretrained.act_postprocess3[0:2](layer_3)
|
117 |
+
layer_4 = pretrained.act_postprocess4[0:2](layer_4)
|
118 |
+
|
119 |
+
unflatten = nn.Sequential(
|
120 |
+
nn.Unflatten(
|
121 |
+
2,
|
122 |
+
torch.Size(
|
123 |
+
[
|
124 |
+
h // pretrained.model.patch_size[1],
|
125 |
+
w // pretrained.model.patch_size[0],
|
126 |
+
]
|
127 |
+
),
|
128 |
+
)
|
129 |
+
)
|
130 |
+
|
131 |
+
if layer_1.ndim == 3:
|
132 |
+
layer_1 = unflatten(layer_1)
|
133 |
+
if layer_2.ndim == 3:
|
134 |
+
layer_2 = unflatten(layer_2)
|
135 |
+
if layer_3.ndim == 3:
|
136 |
+
layer_3 = unflatten(layer_3)
|
137 |
+
if layer_4.ndim == 3:
|
138 |
+
layer_4 = unflatten(layer_4)
|
139 |
+
|
140 |
+
layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
|
141 |
+
layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
|
142 |
+
layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
|
143 |
+
layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
|
144 |
+
|
145 |
+
return layer_1, layer_2, layer_3, layer_4
|
146 |
+
|
147 |
+
|
148 |
+
def _resize_pos_embed(self, posemb, gs_h, gs_w):
|
149 |
+
posemb_tok, posemb_grid = (
|
150 |
+
posemb[:, : self.start_index],
|
151 |
+
posemb[0, self.start_index :],
|
152 |
+
)
|
153 |
+
|
154 |
+
gs_old = int(math.sqrt(len(posemb_grid)))
|
155 |
+
|
156 |
+
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
|
157 |
+
posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
|
158 |
+
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
|
159 |
+
|
160 |
+
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
|
161 |
+
|
162 |
+
return posemb
|
163 |
+
|
164 |
+
|
165 |
+
def forward_flex(self, x):
|
166 |
+
b, c, h, w = x.shape
|
167 |
+
|
168 |
+
pos_embed = self._resize_pos_embed(
|
169 |
+
self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
|
170 |
+
)
|
171 |
+
|
172 |
+
B = x.shape[0]
|
173 |
+
|
174 |
+
if hasattr(self.patch_embed, "backbone"):
|
175 |
+
x = self.patch_embed.backbone(x)
|
176 |
+
if isinstance(x, (list, tuple)):
|
177 |
+
x = x[-1] # last feature if backbone outputs list/tuple of features
|
178 |
+
|
179 |
+
x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
|
180 |
+
|
181 |
+
if getattr(self, "dist_token", None) is not None:
|
182 |
+
cls_tokens = self.cls_token.expand(
|
183 |
+
B, -1, -1
|
184 |
+
) # stole cls_tokens impl from Phil Wang, thanks
|
185 |
+
dist_token = self.dist_token.expand(B, -1, -1)
|
186 |
+
x = torch.cat((cls_tokens, dist_token, x), dim=1)
|
187 |
+
else:
|
188 |
+
cls_tokens = self.cls_token.expand(
|
189 |
+
B, -1, -1
|
190 |
+
) # stole cls_tokens impl from Phil Wang, thanks
|
191 |
+
x = torch.cat((cls_tokens, x), dim=1)
|
192 |
+
|
193 |
+
x = x + pos_embed
|
194 |
+
x = self.pos_drop(x)
|
195 |
+
|
196 |
+
for blk in self.blocks:
|
197 |
+
x = blk(x)
|
198 |
+
|
199 |
+
x = self.norm(x)
|
200 |
+
|
201 |
+
return x
|
202 |
+
|
203 |
+
|
204 |
+
def get_readout_oper(vit_features, features, use_readout, start_index=1):
|
205 |
+
if use_readout == "ignore":
|
206 |
+
readout_oper = [Slice(start_index)] * len(features)
|
207 |
+
elif use_readout == "add":
|
208 |
+
readout_oper = [AddReadout(start_index)] * len(features)
|
209 |
+
elif use_readout == "project":
|
210 |
+
readout_oper = [
|
211 |
+
ProjectReadout(vit_features, start_index) for out_feat in features
|
212 |
+
]
|
213 |
+
else:
|
214 |
+
assert (
|
215 |
+
False
|
216 |
+
), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
|
217 |
+
|
218 |
+
return readout_oper
|
219 |
+
|
220 |
+
|
221 |
+
def _make_vit_b16_backbone(
|
222 |
+
model,
|
223 |
+
features=[96, 192, 384, 768],
|
224 |
+
size=[384, 384],
|
225 |
+
hooks=[2, 5, 8, 11],
|
226 |
+
vit_features=768,
|
227 |
+
use_readout="ignore",
|
228 |
+
start_index=1,
|
229 |
+
enable_attention_hooks=False,
|
230 |
+
):
|
231 |
+
pretrained = nn.Module()
|
232 |
+
|
233 |
+
pretrained.model = model
|
234 |
+
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
|
235 |
+
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
|
236 |
+
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
|
237 |
+
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
|
238 |
+
|
239 |
+
pretrained.activations = activations
|
240 |
+
|
241 |
+
if enable_attention_hooks:
|
242 |
+
pretrained.model.blocks[hooks[0]].attn.register_forward_hook(
|
243 |
+
get_attention("attn_1")
|
244 |
+
)
|
245 |
+
pretrained.model.blocks[hooks[1]].attn.register_forward_hook(
|
246 |
+
get_attention("attn_2")
|
247 |
+
)
|
248 |
+
pretrained.model.blocks[hooks[2]].attn.register_forward_hook(
|
249 |
+
get_attention("attn_3")
|
250 |
+
)
|
251 |
+
pretrained.model.blocks[hooks[3]].attn.register_forward_hook(
|
252 |
+
get_attention("attn_4")
|
253 |
+
)
|
254 |
+
pretrained.attention = attention
|
255 |
+
|
256 |
+
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
|
257 |
+
|
258 |
+
# 32, 48, 136, 384
|
259 |
+
pretrained.act_postprocess1 = nn.Sequential(
|
260 |
+
readout_oper[0],
|
261 |
+
Transpose(1, 2),
|
262 |
+
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
263 |
+
nn.Conv2d(
|
264 |
+
in_channels=vit_features,
|
265 |
+
out_channels=features[0],
|
266 |
+
kernel_size=1,
|
267 |
+
stride=1,
|
268 |
+
padding=0,
|
269 |
+
),
|
270 |
+
nn.ConvTranspose2d(
|
271 |
+
in_channels=features[0],
|
272 |
+
out_channels=features[0],
|
273 |
+
kernel_size=4,
|
274 |
+
stride=4,
|
275 |
+
padding=0,
|
276 |
+
bias=True,
|
277 |
+
dilation=1,
|
278 |
+
groups=1,
|
279 |
+
),
|
280 |
+
)
|
281 |
+
|
282 |
+
pretrained.act_postprocess2 = nn.Sequential(
|
283 |
+
readout_oper[1],
|
284 |
+
Transpose(1, 2),
|
285 |
+
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
286 |
+
nn.Conv2d(
|
287 |
+
in_channels=vit_features,
|
288 |
+
out_channels=features[1],
|
289 |
+
kernel_size=1,
|
290 |
+
stride=1,
|
291 |
+
padding=0,
|
292 |
+
),
|
293 |
+
nn.ConvTranspose2d(
|
294 |
+
in_channels=features[1],
|
295 |
+
out_channels=features[1],
|
296 |
+
kernel_size=2,
|
297 |
+
stride=2,
|
298 |
+
padding=0,
|
299 |
+
bias=True,
|
300 |
+
dilation=1,
|
301 |
+
groups=1,
|
302 |
+
),
|
303 |
+
)
|
304 |
+
|
305 |
+
pretrained.act_postprocess3 = nn.Sequential(
|
306 |
+
readout_oper[2],
|
307 |
+
Transpose(1, 2),
|
308 |
+
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
309 |
+
nn.Conv2d(
|
310 |
+
in_channels=vit_features,
|
311 |
+
out_channels=features[2],
|
312 |
+
kernel_size=1,
|
313 |
+
stride=1,
|
314 |
+
padding=0,
|
315 |
+
),
|
316 |
+
)
|
317 |
+
|
318 |
+
pretrained.act_postprocess4 = nn.Sequential(
|
319 |
+
readout_oper[3],
|
320 |
+
Transpose(1, 2),
|
321 |
+
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
322 |
+
nn.Conv2d(
|
323 |
+
in_channels=vit_features,
|
324 |
+
out_channels=features[3],
|
325 |
+
kernel_size=1,
|
326 |
+
stride=1,
|
327 |
+
padding=0,
|
328 |
+
),
|
329 |
+
nn.Conv2d(
|
330 |
+
in_channels=features[3],
|
331 |
+
out_channels=features[3],
|
332 |
+
kernel_size=3,
|
333 |
+
stride=2,
|
334 |
+
padding=1,
|
335 |
+
),
|
336 |
+
)
|
337 |
+
|
338 |
+
pretrained.model.start_index = start_index
|
339 |
+
pretrained.model.patch_size = [16, 16]
|
340 |
+
|
341 |
+
# We inject this function into the VisionTransformer instances so that
|
342 |
+
# we can use it with interpolated position embeddings without modifying the library source.
|
343 |
+
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
|
344 |
+
pretrained.model._resize_pos_embed = types.MethodType(
|
345 |
+
_resize_pos_embed, pretrained.model
|
346 |
+
)
|
347 |
+
|
348 |
+
return pretrained
|
349 |
+
|
350 |
+
|
351 |
+
def _make_vit_b_rn50_backbone(
|
352 |
+
model,
|
353 |
+
features=[256, 512, 768, 768],
|
354 |
+
size=[384, 384],
|
355 |
+
hooks=[0, 1, 8, 11],
|
356 |
+
vit_features=768,
|
357 |
+
use_vit_only=False,
|
358 |
+
use_readout="ignore",
|
359 |
+
start_index=1,
|
360 |
+
enable_attention_hooks=False,
|
361 |
+
):
|
362 |
+
pretrained = nn.Module()
|
363 |
+
|
364 |
+
pretrained.model = model
|
365 |
+
|
366 |
+
if use_vit_only == True:
|
367 |
+
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
|
368 |
+
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
|
369 |
+
else:
|
370 |
+
pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
|
371 |
+
get_activation("1")
|
372 |
+
)
|
373 |
+
pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
|
374 |
+
get_activation("2")
|
375 |
+
)
|
376 |
+
|
377 |
+
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
|
378 |
+
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
|
379 |
+
|
380 |
+
if enable_attention_hooks:
|
381 |
+
pretrained.model.blocks[2].attn.register_forward_hook(get_attention("attn_1"))
|
382 |
+
pretrained.model.blocks[5].attn.register_forward_hook(get_attention("attn_2"))
|
383 |
+
pretrained.model.blocks[8].attn.register_forward_hook(get_attention("attn_3"))
|
384 |
+
pretrained.model.blocks[11].attn.register_forward_hook(get_attention("attn_4"))
|
385 |
+
pretrained.attention = attention
|
386 |
+
|
387 |
+
pretrained.activations = activations
|
388 |
+
|
389 |
+
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
|
390 |
+
|
391 |
+
if use_vit_only == True:
|
392 |
+
pretrained.act_postprocess1 = nn.Sequential(
|
393 |
+
readout_oper[0],
|
394 |
+
Transpose(1, 2),
|
395 |
+
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
396 |
+
nn.Conv2d(
|
397 |
+
in_channels=vit_features,
|
398 |
+
out_channels=features[0],
|
399 |
+
kernel_size=1,
|
400 |
+
stride=1,
|
401 |
+
padding=0,
|
402 |
+
),
|
403 |
+
nn.ConvTranspose2d(
|
404 |
+
in_channels=features[0],
|
405 |
+
out_channels=features[0],
|
406 |
+
kernel_size=4,
|
407 |
+
stride=4,
|
408 |
+
padding=0,
|
409 |
+
bias=True,
|
410 |
+
dilation=1,
|
411 |
+
groups=1,
|
412 |
+
),
|
413 |
+
)
|
414 |
+
|
415 |
+
pretrained.act_postprocess2 = nn.Sequential(
|
416 |
+
readout_oper[1],
|
417 |
+
Transpose(1, 2),
|
418 |
+
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
419 |
+
nn.Conv2d(
|
420 |
+
in_channels=vit_features,
|
421 |
+
out_channels=features[1],
|
422 |
+
kernel_size=1,
|
423 |
+
stride=1,
|
424 |
+
padding=0,
|
425 |
+
),
|
426 |
+
nn.ConvTranspose2d(
|
427 |
+
in_channels=features[1],
|
428 |
+
out_channels=features[1],
|
429 |
+
kernel_size=2,
|
430 |
+
stride=2,
|
431 |
+
padding=0,
|
432 |
+
bias=True,
|
433 |
+
dilation=1,
|
434 |
+
groups=1,
|
435 |
+
),
|
436 |
+
)
|
437 |
+
else:
|
438 |
+
pretrained.act_postprocess1 = nn.Sequential(
|
439 |
+
nn.Identity(), nn.Identity(), nn.Identity()
|
440 |
+
)
|
441 |
+
pretrained.act_postprocess2 = nn.Sequential(
|
442 |
+
nn.Identity(), nn.Identity(), nn.Identity()
|
443 |
+
)
|
444 |
+
|
445 |
+
pretrained.act_postprocess3 = nn.Sequential(
|
446 |
+
readout_oper[2],
|
447 |
+
Transpose(1, 2),
|
448 |
+
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
449 |
+
nn.Conv2d(
|
450 |
+
in_channels=vit_features,
|
451 |
+
out_channels=features[2],
|
452 |
+
kernel_size=1,
|
453 |
+
stride=1,
|
454 |
+
padding=0,
|
455 |
+
),
|
456 |
+
)
|
457 |
+
|
458 |
+
pretrained.act_postprocess4 = nn.Sequential(
|
459 |
+
readout_oper[3],
|
460 |
+
Transpose(1, 2),
|
461 |
+
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
462 |
+
nn.Conv2d(
|
463 |
+
in_channels=vit_features,
|
464 |
+
out_channels=features[3],
|
465 |
+
kernel_size=1,
|
466 |
+
stride=1,
|
467 |
+
padding=0,
|
468 |
+
),
|
469 |
+
nn.Conv2d(
|
470 |
+
in_channels=features[3],
|
471 |
+
out_channels=features[3],
|
472 |
+
kernel_size=3,
|
473 |
+
stride=2,
|
474 |
+
padding=1,
|
475 |
+
),
|
476 |
+
)
|
477 |
+
|
478 |
+
pretrained.model.start_index = start_index
|
479 |
+
pretrained.model.patch_size = [16, 16]
|
480 |
+
|
481 |
+
# We inject this function into the VisionTransformer instances so that
|
482 |
+
# we can use it with interpolated position embeddings without modifying the library source.
|
483 |
+
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
|
484 |
+
|
485 |
+
# We inject this function into the VisionTransformer instances so that
|
486 |
+
# we can use it with interpolated position embeddings without modifying the library source.
|
487 |
+
pretrained.model._resize_pos_embed = types.MethodType(
|
488 |
+
_resize_pos_embed, pretrained.model
|
489 |
+
)
|
490 |
+
|
491 |
+
return pretrained
|
492 |
+
|
493 |
+
|
494 |
+
def _make_pretrained_vitb_rn50_384(
|
495 |
+
pretrained,
|
496 |
+
use_readout="ignore",
|
497 |
+
hooks=None,
|
498 |
+
use_vit_only=False,
|
499 |
+
enable_attention_hooks=False,
|
500 |
+
):
|
501 |
+
model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
|
502 |
+
|
503 |
+
hooks = [0, 1, 8, 11] if hooks == None else hooks
|
504 |
+
return _make_vit_b_rn50_backbone(
|
505 |
+
model,
|
506 |
+
features=[256, 512, 768, 768],
|
507 |
+
size=[384, 384],
|
508 |
+
hooks=hooks,
|
509 |
+
use_vit_only=use_vit_only,
|
510 |
+
use_readout=use_readout,
|
511 |
+
enable_attention_hooks=enable_attention_hooks,
|
512 |
+
)
|
513 |
+
|
514 |
+
|
515 |
+
def _make_pretrained_vitl16_384(
|
516 |
+
pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
|
517 |
+
):
|
518 |
+
model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
|
519 |
+
|
520 |
+
hooks = [5, 11, 17, 23] if hooks == None else hooks
|
521 |
+
return _make_vit_b16_backbone(
|
522 |
+
model,
|
523 |
+
features=[256, 512, 1024, 1024],
|
524 |
+
hooks=hooks,
|
525 |
+
vit_features=1024,
|
526 |
+
use_readout=use_readout,
|
527 |
+
enable_attention_hooks=enable_attention_hooks,
|
528 |
+
)
|
529 |
+
|
530 |
+
|
531 |
+
def _make_pretrained_vitb16_384(
|
532 |
+
pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
|
533 |
+
):
|
534 |
+
model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
|
535 |
+
|
536 |
+
hooks = [2, 5, 8, 11] if hooks == None else hooks
|
537 |
+
return _make_vit_b16_backbone(
|
538 |
+
model,
|
539 |
+
features=[96, 192, 384, 768],
|
540 |
+
hooks=hooks,
|
541 |
+
use_readout=use_readout,
|
542 |
+
enable_attention_hooks=enable_attention_hooks,
|
543 |
+
)
|
544 |
+
|
545 |
+
|
546 |
+
def _make_pretrained_deitb16_384(
|
547 |
+
pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
|
548 |
+
):
|
549 |
+
model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
|
550 |
+
|
551 |
+
hooks = [2, 5, 8, 11] if hooks == None else hooks
|
552 |
+
return _make_vit_b16_backbone(
|
553 |
+
model,
|
554 |
+
features=[96, 192, 384, 768],
|
555 |
+
hooks=hooks,
|
556 |
+
use_readout=use_readout,
|
557 |
+
enable_attention_hooks=enable_attention_hooks,
|
558 |
+
)
|
559 |
+
|
560 |
+
|
561 |
+
def _make_pretrained_deitb16_distil_384(
|
562 |
+
pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
|
563 |
+
):
|
564 |
+
model = timm.create_model(
|
565 |
+
"vit_deit_base_distilled_patch16_384", pretrained=pretrained
|
566 |
+
)
|
567 |
+
|
568 |
+
hooks = [2, 5, 8, 11] if hooks == None else hooks
|
569 |
+
return _make_vit_b16_backbone(
|
570 |
+
model,
|
571 |
+
features=[96, 192, 384, 768],
|
572 |
+
hooks=hooks,
|
573 |
+
use_readout=use_readout,
|
574 |
+
start_index=2,
|
575 |
+
enable_attention_hooks=enable_attention_hooks,
|
576 |
+
)
|